Knowledge Management (KM) processes are essential for organizations, allowing them to effectively capture, store, and use their knowledge to make informed decisions. Modern enterprises use computerized systems and relational databases to manage their operational processes. However, a significant challenge remains in transforming insights found in digital documents into actionable data models without overloading business analysts or necessitating constant updates and modifications. This work introduces a method for modeling dynamic environments using a knowledge base. The approach involves creating a world model within a relational database that can be updated using Structured Query Language (SQL) expressions derived from documents that describe changes in that world. The techniques discussed include using agents and Large Language Models (LLMs) to generate SQL commands to keep the database current. The proposed world model aims to remain sufficiently generic and adaptable to handle a variety of entities and relationships across multiple organizational domains. Representing events, objects, and their interactions in a flexible structure ensures that real-world transformations are accurately mirrored in the database. This versatility allows the model to be implemented in different sectors without significantly modifying the underlying data architecture. Integrating these processes with advanced language models, such as ChatGPT, aims to improve the generation of data models and streamline the KM workflow by automating the interpretation of explicit knowledge. This integration of language models and relational databases is intended to enhance the organization, storage, and retrieval of insights, thereby reducing manual effort and improving the knowledge base’s adaptability to changing needs. Overall, the proposed solution seeks to leverage LLMs to assist in modeling data and managing knowledge from explicit sources, providing a practical framework for organizations looking to stay competitive in evolving environments.
Serrado et al. (Fri,) studied this question.